Top 10 ETL Testing Terms Every Beginner Should Know

 Here are the Top 10 ETL (Extract, Transform, Load) Testing Terms every beginner should know. These terms form the foundation for understanding how data moves and is validated in data pipelines and warehouses:


🔟 Top 10 ETL Testing Terms

1. Source System

The original data storage location from where data is extracted.


📌 Example: A transactional database like MySQL, Oracle, or an Excel file.


2. Staging Area

A temporary storage space where raw data is first placed before transformation.


📌 Why it matters: Helps in validating the extracted data before applying business rules.


3. Data Mapping

The blueprint that defines how fields from the source system map to the target system.


📌 Used for: Creating test cases and ensuring data integrity from source to destination.


4. Transformation Rules

Logic applied to convert raw data into meaningful or business-friendly formats.


📌 Examples: Date formatting, aggregations, data type conversions.


5. Data Warehouse

The final target system where transformed data is loaded for reporting and analytics.


📌 Includes: Snowflake, Redshift, Google BigQuery, etc.


6. Data Reconciliation

A process of comparing source data with target data to ensure completeness and accuracy.


📌 Key test type: Row count check, value match check.


7. Data Quality Checks

Validations to ensure data is:


Accurate


Complete


Consistent


📌 Examples: Null checks, duplicate checks, format validation.


8. Incremental Load

Loading only new or changed data (delta) instead of the full dataset every time.


📌 Important for: Large datasets and improving performance.


9. Metadata

Information about the data, like data types, column names, length, constraints.


📌 Why it matters: Testing ensures metadata is consistent across systems.


10. ETL Job Scheduling

Automated execution of ETL processes using tools like Apache Airflow, Informatica, or AWS Glue.


📌 You test: Whether jobs run on time and fail gracefully if errors occur.


✅ Bonus Terms (Worth Knowing)

Data Lineage: The path data takes from source to target.


Surrogate Key: A system-generated unique key used in dimension tables.


SCD (Slowly Changing Dimension): Handling changes in dimensional data over time.


📦 Summary Table

Term Description

Source System Original data location

Staging Area Temporary storage for raw data

Data Mapping Field-level mapping between source and target

Transformation Rules Logic for converting data

Data Warehouse Final destination for data

Data Reconciliation Verifying data consistency

Data Quality Checks Ensuring accuracy, completeness

Incremental Load Loading only new/updated data

Metadata Data about the data

ETL Job Scheduling Running ETL jobs on schedule automatically

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